Racial disparities in treatment and outcomes between non-Hispanic Black and non-Hispanic White women with nonmetastatic inflammatory breast cancer.

PURPOSE: The incidence rate of inflammatory breast cancer (IBC) is higher among non-Hispanic Black (NHB) than non-Hispanic White (NHW) women. We examined the differences in treatment and outcomes between NHB and NHW women with IBC, accounting for demographic, clinicopathological, and socioeconomic factors. METHODS: We collected data from the Surveillance, Epidemiology, and End Results database for NHB and NHW women with IBC diagnosed between 2010-2016. We analyzed the odds of receiving chemotherapy, radiation, and surgery between NHB and NHW women. We evaluated overall survival (OS) with Kaplan-Meier methods and Cox proportional hazards methods. Competing risk analysis was used to compare the risk of breast cancer death between NHB and NHW women. We also evaluated the magnitude of survival disparities within the strata of demographic, socioeconomic, and treatment factors. RESULTS: Among 1,652 NHW and 371 NHB women with IBC, the odds of receiving chemotherapy, surgery, and radiation were similar for NHB and NHW. After 39-month follow-up, the median OS was 40 and 81 months for NHB and NHW, respectively (p < 0.0001). The risk of breast cancer death was higher for NHB than NHW women (5-year risk of breast cancer death, 51% vs. 35%, p < 0.0001). CONCLUSION: After adjustment for demographic, clinicopathological, and socioeconomic factors; NHB women with IBC had similar odds of receiving surgery, chemotherapy, and radiation therapy, but were more likely to die of the disease compared to their NHW counterparts. Our findings suggest the presence of masked tumor biology, treatment, or socioeconomic factors associated with race that can lead to worse IBC outcomes.

Isotopic niche partitioning and individual specialization in an Arctic raptor guild.

Intra- and inter-specific resource partitioning within predator communities is a fundamental component of trophic ecology, and one proposed mechanism for how populations partition resources is through individual niche variation. The Niche Variation Hypothesis (NVH) predicts that inter-individual trait variation leads to functional trade-offs in foraging efficiency, resulting in populations composed of individual dietary specialists. The degree to which niche specialization persists within a population is plastic and responsive to fluctuating resource availability. We quantified niche overlap and tested the NVH within an Arctic raptor guild, focusing on three species that employ different foraging strategies: golden eagles (generalists); gyrfalcons (facultative specialists); and rough-legged hawks (specialists). Tundra ecosystems exhibit cyclic populations of arvicoline rodents (lemmings and voles), providing a unique system in which to examine predator diet in response to interannual fluctuations in resource availability. Using blood δ13C and δ15N values from 189 raptor nestlings on Alaska's Seward Peninsula (2014-2019), we calculated isotopic niche width and used Bayesian stable isotope mixing models (BSIMMs) to characterize individual specialization and test the NVH. Nest-level specialization estimated from stable isotopes was strongly correlated with indices of specialization based on camera trap data. We observed a high degree of isotopic niche overlap between the three species and gyrfalcons displayed a positive relationship between individual specialization and population niche width on an interannual basis consistent with the NVH. Our findings suggest plasticity in niche specialization may reduce intra- and inter-specific resource competition under dynamic ecological conditions.

A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity.

Community occupancy models estimate species-specific parameters while sharing information across species by treating parameters as sampled from a common distribution. When communities consist of discrete groups, shrinkage of estimates toward the community mean can mask differences among groups. Infinite-mixture models using a Dirichlet process (DP) distribution, in which the number of latent groups is estimated from the data, have been proposed as a solution. In addition to community structure, these models estimate species similarity, which allows testing hypotheses about whether traits drive species response to environmental conditions. We develop a community occupancy model (COM) using a DP distribution to model species-level parameters. Because clustering algorithms are sensitive to dimensionality and distinctiveness of clusters, we conducted a simulation study to explore performance of the DP-COM with different dimensions (i.e., different numbers of model parameters with species-level DP random effects) and under varying cluster differences. Because the DP-COM is computationally expensive, we compared its estimates to a COM with a normal random species effect. We further applied the DP-COM model to a bird data set from Uganda. Estimates of the number of clusters and species cluster identity improved with increasing difference among clusters and increasing dimensions of the DP; but the number of clusters was always overestimated. Estimates of number of sites occupied and species and community-level covariate coefficients on occupancy probability were generally unbiased with (near-) nominal 95% Bayesian Credible Interval coverage. Accuracy of estimates from the normal and the DP-COM was similar. The DP-COM clustered 166 bird species into 27 clusters regarding their affiliation with open or woodland habitat and distance to oil wells. Estimates of covariate coefficients were similar between a normal and the DP-COM. Except sunbirds, species within a family were not more similar in their response to these covariates than the overall community. Given that estimates were consistent between the normal and the DP-COM, and considering the computational burden for the DP models, we recommend using the DP-COM only when the analysis focuses on community structure and species similarity, as these quantities can only be obtained under the DP-COM.

Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor.

Bayesian stable isotope mixing models (BSIMMs) for δ13 C and δ15 N can be a useful tool to reconstruct diets, characterize trophic relationships, and assess spatiotemporal variation in food webs. However, use of this approach typically requires a priori knowledge on the level of enrichment occurring between the diet and tissue of the consumer being sampled (i.e. a trophic discrimination factor or TDF). Trophic discrimination factors derived from captive feeding studies are highly variable, and it is challenging to select the appropriate TDF for diet estimation in wild populations. We introduce a novel method for estimating TDFs in a wild population-a proportionally balanced equation that uses high-precision diet estimates from nest cameras installed on a subset of nests in lieu of a controlled feeding study (TDFCAM ). We tested the ability of BSIMMs to characterize diet in a free-living population of gyrfalcon Falco rusticolus nestlings by comparing model output to high-precision nest camera diet estimates. We analysed the performance of models formulated with a TDFCAM against other relevant TDFs and assessed model sensitivity to an informative prior. We applied the most parsimonious model inputs to a larger sample to analyse broad-scale temporal dietary trends. Bayesian stable isotope mixing models fitted with a TDFCAM and uninformative prior had the best agreement with nest camera data, outperforming TDFs derived from captive feeding studies. BSIMMs produced with a TDFCAM produced reliable diet estimates at the nest level and accurately identified significant temporal shifts in gyrfalcon diet within and between years. Our method of TDF estimation produced more accurate estimates of TDFs in a wild population than traditional approaches, consequently improving BSIMM diet estimates. We demonstrate how BSIMMs can complement a high-precision diet study by expanding its spatiotemporal scope of inference and recommend this integrative methodology as a powerful tool for future trophic studies.

Dynamic occupancy models for explicit colonization processes.

The dynamic, multi-season occupancy model framework has become a popular tool for modeling open populations with occupancies that change over time through local colonizations and extinctions. However, few versions of the model relate these probabilities to the occupancies of neighboring sites or patches. We present a modeling framework that incorporates this information and is capable of describing a wide variety of spatiotemporal colonization and extinction processes. A key feature of the model is that it is based on a simple set of small-scale rules describing how the process evolves. The result is a dynamic process that can account for complicated large-scale features. In our model, a site is more likely to be colonized if more of its neighbors were previously occupied and if it provides more appealing environmental characteristics than its neighboring sites. Additionally, a site without occupied neighbors may also become colonized through the inclusion of a long-distance dispersal process. Although similar model specifications have been developed for epidemiological applications, ours formally accounts for detectability using the well-known occupancy modeling framework. After demonstrating the viability and potential of this new form of dynamic occupancy model in a simulation study, we use it to obtain inference for the ongoing Common Myna (Acridotheres tristis) invasion in South Africa. Our results suggest that the Common Myna continues to enlarge its distribution and its spread via short distance movement, rather than long-distance dispersal. Overall, this new modeling framework provides a powerful tool for managers examining the drivers of colonization including short- vs. long-distance dispersal, habitat quality, and distance from source populations.

Estimating demographic parameters using a combination of known-fate and open N-mixture models.

Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark-resight data sets. We provide implementations in both the BUGS language and an R package.

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas.

Determining the range of a species and exploring species--habitat associations are central questions in ecology and can be answered by analyzing presence--absence data. Often, both the sampling of sites and the desired area of inference involve neighboring sites; thus, positive spatial autocorrelation between these sites is expected. Using survey data for the Southern Ground Hornbill (Bucorvus leadbeateri) from the Southern African Bird Atlas Project, we compared advantages and disadvantages of three increasingly complex models for species occupancy: an occupancy model that accounted for nondetection but assumed all sites were independent, and two spatial occupancy models that accounted for both nondetection and spatial autocorrelation. We modeled the spatial autocorrelation with an intrinsic conditional autoregressive (ICAR) model and with a restricted spatial regression (RSR) model. Both spatial models can readily be applied to any other gridded, presence--absence data set using a newly introduced R package. The RSR model provided the best inference and was able to capture small-scale variation that the other models did not. It showed that ground hornbills are strongly dependent on protected areas in the north of their South African range, but less so further south. The ICAR models did not capture any spatial autocorrelation in the data, and they took an order, of magnitude longer than the RSR models to run. Thus, the RSR occupancy model appears to be an attractive choice for modeling occurrences at large spatial domains, while accounting for imperfect detection and spatial autocorrelation.

Exploring the Use of Natural Language Processing to Understand Emotions of Trainees and Faculty Regarding Entrustable Professional Activity Assessments.

Background In medical education, artificial intelligence techniques such as natural language processing (NLP) are starting to be used to capture and analyze emotions through written text. Objective To explore the application of NLP techniques to understand resident and faculty emotions related to entrustable professional activity (EPA) assessments. Methods Open-ended text data from a survey on emotions toward EPA assessments were analyzed. Respondents were residents and faculty from pediatrics (Peds), general surgery (GS), and emergency medicine (EM), recruited for a larger emotions study in 2023. Participants wrote about their emotions related to receiving/completing EPA assessments. We analyzed the frequency of words rated as positive via a validated sentiment lexicon used in NLP studies. Specifically, we were interested if the count of positive words varied as a function of group membership (faculty, resident), specialty (Peds, GS, EM), gender (man, woman, nonbinary), or visible minority status (yes, no, omit). Results A total of 66 text responses (30 faculty, 36 residents) contained text data useful for sentiment analysis. We analyzed the difference in the count of words categorized as positive across group, specialty, gender, and being a visible minority. Specialty was the only category revealing significant differences via a bootstrapped Poisson regression model with GS responses containing fewer positive words than EM responses. Conclusions By analyzing text data to understand emotions of residents and faculty through an NLP approach, we identified differences in EPA assessment-related emotions of residents versus faculty, and differences across specialties.

Don't worry, they get the idea: instructions have no impact on dehumanization ratings on the Ascent of Human Scale.

A common method for assessing blatant dehumanization asks participants to rate "how evolved" they think members of various social groups are using the Ascent of Human scale (AOH) that transitions in stages from a crawling ape to a fully upright modern human. However, little is known about how task instructions affect participant ratings. In this pre-registered study, participants saw alternative forms of instruction including the traditional instructions emphasizing "evolution", a prompt without any reference to evolution, and a prompt that clearly explained that the scale assesses dehumanization. Instruction type had no effect on dehumanization ratings on the AOH scale. These results support the idea that the AOH scale is a robust means of assessing blatant dehumanization.

Paclitaxel-induced myocarditis presenting as new-onset heart failure.

Myocarditis with systolic dysfunction is not typically associated with paclitaxel use. Here, we present a case of paclitaxel-induced myocarditis with systolic dysfunction developing after two cycles of carboplatin/paclitaxel in a woman with uterine papillary serous carcinoma and no cardiac risk factors. Myocarditis was diagnosed by cardiac MRI. The management of paclitaxel-induced myocarditis includes intravenous diuresis and initiation of heart failure with reduced ejection fraction guideline-directed medical therapy. Cessation of paclitaxel is also recommended in these patients.

Evaluating the Risk Landscape of Hawaiian Monk Seal Exposure to Toxoplasma gondii.

Toxoplasmosis is a disease of primary concern for Hawaiian monk seals (Neomonachus schauinslandi), due to its apparently acute lethality and especially heavy impacts on breeding female seals. The disease-causing parasite, Toxoplasma gondii, depends on cats to complete its life cycle; thus, in order to understand how this pathogen infects marine mammals, it is essential to understand aspects of the terrestrial ecosystem and land-to-sea transport. In this study, we constructed a three-tiered model to assess risk of Hawaiian monk seal exposure to T. gondii oocysts: (1) oocyst contamination as a function of cat population characteristics; (2) land-to-sea transport of oocysts as a function of island hydrology, and (3) seal exposure as a function of habitat and space use. We were able to generate risk maps highlighting watersheds contributing the most to oocyst contamination of Hawaiian monk seal habitat. Further, the model showed that free-roaming cats most associated with humans (pets or strays often supplementally fed by people) were able to achieve high densities leading to high levels of oocyst contamination and elevated risk of T. gondii exposure.

Accommodating species identification errors in transect surveys.

Ecologists often use transect surveys to estimate the density and abundance of animal populations. Errors in species classification are often evident in such surveys, yet few statistical methods exist to properly account for them. In this paper, we examine biases that result from species misidentification when ignored, and we develop statistical models to provide unbiased estimates of density in the face of such errors. Our approach treats true species identity as a latent variable and requires auxiliary information on the misclassification process (such as informative priors, experiments using known species, or a double-observer protocol). We illustrate our approach with simulated census data and with double-observer survey data for ice-associated seals in the Bering Sea. For the seal analysis, we integrated misclassification into a model-based framework for distance-sampling data. The simulated data analysis demonstrated reliable estimation of animal density when there are experimental data to inform misclassification rates; double-observer protocols provided robust inference when there were "unknown" species observations but no outright misclassification, or when misclassification probabilities were symmetric and a symmetry constraint was imposed during estimation. Under our modeling framework, we obtained reasonable apparent densities of seal species even under considerable imprecision in species identification. We obtained more reliable inferences when modeling variation in density among transects. We argue that ecologists should often use spatially explicit models to account for differences in species distributions when trying to account for species misidentification. Our results support using double-observer sampling protocols that guard against species misclassification (i.e., by recording uncertain observations as "unknown").

Estimating animal resource selection from telemetry data using point process models.

1. Analyses of animal resource selection functions (RSF) using data collected from relocations of individuals via remote telemetry devices have become commonplace. Increasing technological advances, however, have produced statistical challenges in analysing such highly autocorrelated data. Weighted distribution methods have been proposed for analysing RSFs with telemetry data. However, they can be computationally challenging due to an intractable normalizing constant and cannot be aggregated (i.e. collapsed) over time to make space-only inference. 2. In this study, we take a conceptually different approach to modelling animal telemetry data for making RSF inference. We consider the telemetry data to be a realization of a space-time point process. Under the point process paradigm, the times of the relocations are also considered to be random rather than fixed. 3. We show the point process models we propose are a generalization of the weighted distribution telemetry models. By generalizing the weighted model, we can access several numerical techniques for evaluating point process likelihoods that make use of common statistical software. Thus, the analysis methods can be readily implemented by animal ecologists. 4. In addition to ease of computation, the point process models can be aggregated over time by marginalizing over the temporal component of the model. This allows a full range of models to be constructed for RSF analysis at the individual movement level up to the study area level. 5. To demonstrate the analysis of telemetry data with the point process approach, we analysed a data set of telemetry locations from northern fur seals (Callorhinus ursinus) in the Pribilof Islands, Alaska. Both a space-time and an aggregated space-only model were fitted. At the individual level, the space-time analysis showed little selection relative to the habitat covariates. However, at the study area level, the space-only model showed strong selection relative to the covariates.

Reconciling resource utilization and resource selection functions.

1. Analyses based on utilization distributions (UDs) have been ubiquitous in animal space use studies, largely because they are computationally straightforward and relatively easy to employ. Conventional applications of resource utilization functions (RUFs) suggest that estimates of UDs can be used as response variables in a regression involving spatial covariates of interest. 2. It has been claimed that contemporary implementations of RUFs can yield inference about resource selection, although to our knowledge, an explicit connection has not been described. 3. We explore the relationships between RUFs and resource selection functions from a hueristic and simulation perspective. We investigate several sources of potential bias in the estimation of resource selection coefficients using RUFs (e.g. the spatial covariance modelling that is often used in RUF analyses). 4. Our findings illustrate that RUFs can, in fact, serve as approximations to RSFs and are capable of providing inference about resource selection, but only with some modification and under specific circumstances. 5. Using real telemetry data as an example, we provide guidance on which methods for estimating resource selection may be more appropriate and in which situations. In general, if telemetry data are assumed to arise as a point process, then RSF methods may be preferable to RUFs; however, modified RUFs may provide less biased parameter estimates when the data are subject to location error.

Salience Matters: Filler groups on the ascent of human scale impact ratings for target groups.

Researchers using the ascent of human scale (AOH) to study dehumanization typically include filler groups in addition to the main comparator groups, to hide the true intent of the study. However, there is little work examining the impact of filler group choice on dehumanization ratings between groups of interest. Across two studies (including one pre-registered study) we manipulated the salience of a target out-group (i.e., the extent to which the group stood out) by embedding it within lists of other groups. By comparing AOH ratings across three conditions in which the target out-group was either high salience, medium salience, or low salience, we were able to determine the effects of target out-group salience on dehumanization. In study 1, we included participants' in-group (Canadian) in the list, and in study 2, we did not include participants in-group in the list. Results from study 1 showed that group salience had no impact on AOH ratings for the out-group when the participant in-group was included in the list. However, in study 2, when participant in-group was removed from the list, ratings for the out-group in the high salience condition were significantly lower than both the medium and low salience conditions. Implications for both theoretical and methodological issues in investigations using the AOH scale are discussed.

Age-specific reproduction in female Steller sea lions in Southeast Alaska.

Age-, region-, and year-specific estimates of reproduction are needed for monitoring wildlife populations during periods of ecosystem change. Population dynamics of Steller sea lions (Eumetopias jubatus) in Southeast Alaska varied regionally (with high population growth and survival in the north vs. the south) and annually (with reduced adult female survival observed following a severe marine heatwave event), but reproductive performance is currently unknown. We used mark-resighting data from 1006 Steller sea lion females marked as pups at ~3 weeks of age from 1994 to 1995 and from 2001 to 2005 and resighted from 2002 to 2019 (to a maximum age of 25) to examine age-, region-, and year-specific reproduction. In the north versus the south, age of first reproduction was earlier (beginning at age 4 vs. age 5, respectively) but annual birth probabilities of parous females were reduced by 0.05. In an average year pre-heatwave, the proportion of females with pup at the end of the pupping season peaked at ages 12-13 with ~0.60/0.65 (north/south) with pup, ~0.30/0.25 with juvenile, and ~0.10 (both regions) without a dependent. In both regions, reproductive senescence was gradual after age 12: ~0.40, 0.40, and 0.20 of females were in these reproductive states, respectively, by age 20. Correcting for neonatal mortality, true birth probabilities at peak ages were 0.66/0.72 (north/south). No cost of reproduction on female survival was detected, but pup production remained lower (-0.06) after the heatwave event, which if sustained could result in population decline in the south. Reduced pup production and greater retention of juveniles during periods of poor prey conditions may be an important strategy for Steller sea lions in Southeast Alaska, where fine-tuning reproduction based on nutritional status may improve the lifetime probability of producing pups under good conditions in a variable and less productive environment.

TDFCAM: A method for estimating stable isotope trophic discrimination in wild populations.

Stable isotope mixing models (SIMMs) are widely used for characterizing wild animal diets. Such models rely upon using accurate trophic discrimination factors (TDFs) to account for the digestion, incorporation, and assimilation of food. Existing methods to calculate TDFs rely on controlled feeding trials that are time-consuming, often impractical for the study taxon, and may not reflect natural variability of TDFs present in wild populations.We present TDFCAM as an alternative approach to estimating TDFs in wild populations, by using high-precision diet estimates from a secondary methodological source-in this case nest cameras-in lieu of controlled feeding trials, and provide a framework for how and when it should be applied.In this study, we evaluate the TDFCAM approach in three datasets gathered on wild raptor nestlings (gyrfalcons Falco rusticolus; peregrine falcons Falco perigrinus; common buzzards Buteo buteo) comprising contemporaneous δ13C & δ15N stable isotope data and high-quality nest camera dietary data. We formulate Bayesian SIMMs (BSIMMs) incorporating TDFs from TDFCAM and analyze their agreement with nest camera data, comparing model performance with those based on other relevant TDFs. Additionally, we perform sensitivity analyses to characterize TDFCAM variability, and identify ecological and physiological factors contributing to that variability in wild populations.Across species and tissue types, BSIMMs incorporating a TDFCAM outperformed any other TDF tested, producing reliable population-level estimates of diet composition. We demonstrate that applying this approach even with a relatively low sample size (n < 10 individuals) produced more accurate estimates of trophic discrimination than a controlled feeding study conducted on the same species. Between-individual variability in TDFCAM estimates for ∆13C & ∆15 N increased with analytical imprecision in the source dietary data (nest cameras) but was also explained by natural variables in the study population (e.g., nestling nutritional/growth status and dietary composition).TDFCAM is an effective method of estimating trophic discrimination in wild animal populations. Here, we use nest cameras as source dietary data, but this approach is applicable to any high-accuracy method of measuring diet, so long as diet can be monitored over an interval contemporaneous with a tissue's isotopic turnover rate.

Coalitionality shapes moral elevation: evidence from the U.S. Black Lives Matter protest and counter-protest movements.

Witnessing altruistic behaviour can elicit moral elevation, an emotion that motivates prosocial cooperation. This emotion is evoked more strongly when the observer anticipates that other people will be reciprocally cooperative. Coalitionality should therefore moderate feelings of elevation, as whether the observer shares the coalitional affiliation of those observed should influence the observer's assessment of the likelihood that the latter will cooperate with the observer. We examined this thesis in studies contemporaneous with the 2020 Black Lives Matter (BLM) protests. Although BLM protests were predominantly peaceful, they were depicted by conservative media as destructive and antisocial. In two large-scale, pre-registered online studies (total N = 2172), political orientation strongly moderated feelings of state elevation elicited by a video of a peaceful BLM protest (Studies 1 and 2) or a peaceful Back the Blue (BtB) counter-protest (Study 2). Political conservatism predicted less elevation following the BLM video and more elevation following the BtB video. Elevation elicited by the BLM video correlated with preferences to defund police, whereas elevation elicited by the BtB video correlated with preferences to increase police funding. These findings extend prior work on elevation into the area of prosocial cooperation in the context of coalitional conflict.

An integrated path for spatial capture-recapture and animal movement modeling.

Ecologists and conservation biologists increasingly rely on spatial capture-recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.

Exploring the Relationship Between Empathy, Self-Construal Style, and Self-Reported Social Distancing Tendencies During the COVID-19 Pandemic.

Social distancing has become the most prominent measure many countries have implemented to combat the spread of COVID-19. The aim of the current study was to explore the potential role of empathy and self-construal styles, as individual personality traits, on self-reported social distancing. Participants completed the Interpersonal Reactivity Index (a multi-dimensional measure of trait-levels of empathy), the Singelis Self-Construal Scale (a measure of self-construal styles), and were asked to rate their level of social distancing and how much they endorsed social distancing on a five-point Likert-scale. Across a large and diverse sample (with participants collected from Canada, United Kingdom, Sweden, and United States; total n = 967), results showed that trait-levels of empathic concern (EC) and perspective taking (PT) positively correlates with social distancing. However, we did not find evidence to suggest that trait-levels of personal distress correlates with social distancing. We interpret these findings as suggesting that empathy, both its altruistic (EC) and cognitive (PT) dimensions, plays an important role in motivating people to socially distance and should be emphasized during times of crisis. Furthermore, we suggest that emphasizing a person's self-distress during times of crisis may not be an effective approach in promotion social distancing policies (or other prosocial behaviors). We also found that both independence and interdependence self-construal styles positively correlates with social distancing. While we expected the latter result, we did not expect the former. This suggests that more work is needed to fully understand how self-construal styles, along with their cultural level analogs (i.e., Individualism-Collectivism), influences social distancing. Overall, these results provide us with novel multi-national data about the role of individual differences on social distancing tendencies specifically, and human behavior during a global health crisis more generally.